90%
Prediction accuracy
10%-20%
Shrinkage reduction
6-9
Months development
The challenge
Navigating baggage prediction complexities
Since 2016, the airline has struggled significantly with inaccurate carry-on bag predictions, achieving less than 20% reliability in their estimates. This consistently led to inefficient baggage handling processes and frequent flight delays.
Key challenges
Absence of real-time bin space monitoring and feedback mechanisms
Lack of systematic data integration across data sources and systems
Low adoption due to unexplainable predictions
Inflexible batch deployment system
The solution
AI-powered prediction platform for intelligent baggage management
Intelligent data processing
CatBoost Regression decision tree ensemble
Real-time API-driven model updates
Comprehensive feature engineering
Enterprise integration
AWS-based flexible architecture
User-friendly front-end application
Continuous model retraining capability
Implementation approach
1
Foundation
Created flexible architecture
Developed prediction requirements
Integrated diverse data sources
2
Development
Built machine learning models
Applied sampling techniques
Refined from user feedback
3
Deployment
6-9 month design and development phase
Continuous improvement process
3-month implementation
The impact
Data-driven transformation of baggage operations
Looking ahead
Expanding predictive capabilities
Further improving operational streamlining
Enhanced intelligence
Process optimization
Identifying critical high-risk categories
Risk management
